Indian Institute of Technology Indore

About: Indian Institute of Technology Indore is a education organization based out in Indore, Madhya Pradesh, India. It is known for research contribution in the topics: Computer science & Chemistry. The organization has 1606 authors who have published 4803 publications receiving 66500 citations.

Automated Diagnosis of Epilepsy Using Key-Point-Based Local Binary Pattern of EEG Signals

Abstract: The electroencephalogram (EEG) signals are commonly used for diagnosis of epilepsy. In this paper, we present a new methodology for EEG-based automated diagnosis of epilepsy. Our method involves detection of key points at multiple scales in EEG signals using a pyramid of difference of Gaussian filtered signals. Local binary patterns (LBPs) are computed at these key points and the histogram of these patterns are considered as the feature set, which is fed to the support vector machine (SVM) for the classification of EEG signals. The proposed methodology has been investigated for the four well-known classification problems namely, 1) normal and epileptic seizure, 2) epileptic seizure and seizure free, 3) normal, epileptic seizure, and seizure free, and 4) epileptic seizure and nonseizure EEG signals using publically available university of Bonn EEG database. Our experimental results in terms of classification accuracies have been compared with existing methods for the classification of the aforementioned problems. Further, performance evaluation on another EEG dataset shows that our approach is effective for classification of seizure and seizure-free EEG signals. The proposed methodology based on the LBP computed at key points is simple and easy to implement for real-time epileptic seizure detection.

A massive rock and ice avalanche caused the 2021 disaster at Chamoli, Indian Himalaya

Abstract: On 7 Feb 2021, a catastrophic mass flow descended the Ronti Gad, Rishiganga, and Dhauliganga valleys in Chamoli, Uttarakhand, India, causing widespread devastation and severely damaging two hydropower projects. Over 200 people were killed or are missing. Our analysis of satellite imagery, seismic records, numerical model results, and eyewitness videos reveals that ~27x106 m3 of rock and glacier ice collapsed from the steep north face of Ronti Peak. The rock and ice avalanche rapidly transformed into an extraordinarily large and mobile debris flow that transported boulders >20 m in diameter, and scoured the valley walls up to 220 m above the valley floor. The intersection of the hazard cascade with downvalley infrastructure resulted in a disaster, which highlights key questions about adequate monitoring and sustainable development in the Himalaya as well as other remote, high-mountain environments.

A Multi-class EEG-based BCI classification using Multivariate Empirical Mode Decomposition Based Filtering and Riemannian Geometry

Abstract: A brain-computer interface (BCI) facilitates a medium to translate the human motion intentions using electrical brain activity signals such as electroencephalogram (EEG) into control signals. EEG signals are non-stationary and subject specific. A major challenge in BCI research is to classify human motion intentions from non-stationary EEG signals. We propose a novel subject specific multivariate empirical mode decomposition (MEMD) based filtering method, namely, SS-MEMDBF to classify the motor imagery (MI) based EEG signals into multiple classes. The MEMD method simultaneously decomposes the multichannel EEG signals into a group of multivariate intrinsic mode functions (MIMFs). This decomposition enables us to extract the cross-channel information and also localize the specific frequency information. The MIMFs are considered as narrow-band, amplitude and frequency modulated (AFM) signals. The statistical measure, mean frequency has been used to automatically filter the MIMFs to obtain enhanced EEG signals which better represent motor imagery related brainwave modulations over μ and β rhythms. The sample covariance matrix has been computed and used as a feature set. The feature set has been classified into multiple MI tasks using Riemannian geometry. The proposed method has helped achieve mean Kappa value of 0.60 across nine subjects of the BCI competition IV dataset 2A which is superior to all the reported methods.

K∗(892)0 and φ(1020) production in Pb-Pb collisions at sNN =2.76 TeV

Abstract: The yields of the K*(892)(0) and phi(1020) resonances are measured in Pb-Pb collisions at root s(NN) = 2.76 TeV through their hadronic decays using the ALICE detector. The measurements are performed in multiple centrality intervals at mid-rapidity (vertical bar y vertical bar <0.5) in the transverse-momentum ranges 0.3